[Matplotlib-users] making continuous color images with custom colormaps

[Michael H. <mh...@us...>]

I am posting yet another question about colormaps, as I am having 
trouble grasping the fundamentals of the way the color model works in 
Matplotlib.

There are many examples on-line of very nice looking continuous color 
images, such as the one that would be produced by using this code:

/delta = 0.005
extent = (-3,4,-4,3)

x = arange(-3.0, 4.001, delta)
y = arange(-4.0, 3.001, delta)
X, Y = meshgrid(x, y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = (Z1 - Z2) * 10

#normalize the example Z data to be between 0 and 10
Z = ((Z - Z.min())/(Z.max() - Z.min()))*10
jet()
imshow(Z)
show()

/However, I can't find any similar examples for custom colormaps.  Below 
is some test code I wrote to try to understand this.  In it I have 
hard-coded a color dictionary (suitable for use with a 
LinearSegmentedColormap), a color list (suitable for use with a 
ListedColormap), and an array of Z values (appropriate for a Boundary 
norm).  I have tried various combinations of Listed and LinearSegmented 
colormaps, and they either show patches of very discrete colors, or no 
colors, or the resulting image blows up when I call savefig().

My goal here is to display the Z data in a continuous colormap where the 
values are interpolated according to either the color dictionary or 
color list I have defined.

A final side question:  Does a tutorial on the matplotlib color model 
exist anywhere?  This would be a really useful resource for me, and 
perhaps for others.

Code is appended below.

Thanks,

Mike

#!/usr/bin/env python
'''
Trying to figure out how to make a smooth continuous image with my 
custom colormap
'''
from pylab import *
from matplotlib.colors import 
ListedColormap,LinearSegmentedColormap,Normalize,BoundaryNorm

isListed = True

delta = 0.005
extent = (-3,4,-4,3)

x = arange(-3.0, 4.001, delta)
y = arange(-4.0, 3.001, delta)
X, Y = meshgrid(x, y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = (Z1 - Z2) * 10

#normalize the example Z data to be between 0 and 10 (to match my 
colormap data)
Z = ((Z - Z.min())/(Z.max() - Z.min()))*10


cdict = {'blue': [1.0,
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  0.57647058823529407,
                  0.0,
                  0.0,
                  0.0,
                  0.0,
                  0.0,
                  0.0],
         'green': [1.0,
                   1.0,
                   0.80000000000000004,
                   0.90196078431372551,
                   1.0,
                   1.0,
                   1.0,
                   0.78431372549019607,
                   0.56862745098039214,
                   0.0,
                   0.0,
                   0.0],
         'red': [1.0,
                 1.0,
                 0.74901960784313726,
                 0.62745098039215685,
                 0.50196078431372548,
                 0.47843137254901963,
                 1.0,
                 1.0,
                 1.0,
                 1.0,
                 0.78431372549019607,
                 0.50196078431372548]}


clist = array([[ 1.        ,  1.        ,  1.        ],
               [ 1.        ,  1.        ,  1.        ],
               [ 0.74901961,  0.8       ,  1.        ],
               [ 0.62745098,  0.90196078,  1.        ],
               [ 0.50196078,  1.        ,  1.        ],
               [ 0.47843137,  1.        ,  0.57647059],
               [ 1.        ,  1.        ,  0.        ],
               [ 1.        ,  0.78431373,  0.        ],
               [ 1.        ,  0.56862745,  0.        ],
               [ 1.        ,  0.        ,  0.        ],
               [ 0.78431373,  0.        ,  0.        ],
               [ 0.50196078,  0.        ,  0.        ]])

boundaries = array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   
8.,   9.,  10., 13.])